Telecommunications services in Nigeria have entered a new phase of regulatory adjustment as government oversight over digital ...

Background The high prevalence of atrial fibrillation (AF) and its association with cardiovascular (CV) outcomes represent a ...

The Gulf Coast is recognized worldwide for its exceptional fishing opportunities, offering anglers a wide variety of species ...

This study investigated heterogeneous subtypes of non-suicidal self-injury (NSSI) among college students and examined the psychosocial predictors of high-risk profiles to guide precision interventions ...

LLMは「蒸留（distillation；ディスティレーション）」と呼ばれるプロセスをつうじて、ほかのモデルを訓練するためのデータセットを生成できる。このプロセスでは、「生徒」モデルが「教師」モデルの出力を模倣するように学習する。この過程は、LLMの低コスト版を作成する目的で利用されることがあるが、教師モデルのどの特性が生徒モデルに伝達されるかは不明である。

When quality discussions are really classification issues: In many cases, debates about cleaning quality are not primarily ...

Dr. Alan Kuhnle, assistant professor in the computer science and engineering department at Texas A&M University, is using smartphone mobility data collected from anglers to develop machine-learning ...

A research team from Sichuan University has proposed a lightweight and robust entropy-regularized unsupervised domain adaptation framework (LRE-UDAF ...

Published in Microplastics, the study titled “Canonical Spectral Transformation for Raman Spectra Enables High Accuracy AI Identification of Marine Microplastics” introduces a novel data processing ...

What do mosquito populations and physical measurement data have in common? Both lead to a central problem in machine learning: the reliable estimation of class prevalence in the face of changing data.

Researchers have examined the challenge of detecting and classifying dynamic road obstacles for autonomous driving systems ...